Remote sensing approach and geoprocessing tools for estimating wildfire rate of spread

Authors: Douglas Stow*, San Diego State University, Gavin Schag, San Diego State University, Philip Riggan, United States Forest Service
Topics: Remote Sensing, Hazards, Risks, and Disasters, Geographic Information Science and Systems
Keywords: wildfire, fire spread, thermal infrared, remote sensing
Session Type: Poster
Day: 4/4/2019
Start / End Time: 9:55 AM / 11:35 AM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: No File Uploaded

Improving understanding of fire behavior in wildland areas is of interest for fire suppression and evacuations, fire spread modeling, and studying fire effects on ecosystem functioning. An important property of wildfire behavior is rate of spread (ROS). Observing fire behavior and estimating ROS at landscape scales, and over extensive wildland areas, is challenging and has rarely been attempted. We developed an approach to repetitive aerial thermal infrared (ATIR) imaging and designed a sequence of image processing and analysis steps to estimate wildfire ROS and examine landscape controls on ROS. These include geoprocessing individual image frames, delineating active fire front positions, generating fire spread vectors and ROS estimates based on changing front positions, extracting topographic and fuels data in the vicinity of spread vectors, and evaluating relationships between ROS and topographic and fuels covariates. Other than image geoprocessing, most image processing and analysis are supported by image processing and GIS tools within ArcGIS Pro software. These include tools for: brightness enhancement; delineating and smoothing active fire fronts; selecting sample points, determining normal to curve directions and delineating fire spread vectors; computing directional slope from digital elevation models; generating landscape sampling units (LSUs) around spread vectors and extracting landscape covariates from within LSUs; and visualizing time sequences of fire spread in three dimensions. We present examples of the processing flow, ROS estimates, preliminary evaluations of statistical relationships between ROS and slope and fuel covariates, and 3-D visualizations for the Detwiler and Thomas fires that burned in California in 2017.

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